The manually annotated gold-standard segmentation maps are binary, with each pixel corresponding to either an object of interest (retinal vessels, skin lesions, abnormalities) or the background. Thus, for each pixel in an output image, there are four possible outcomes: the pixel belongs to an object of interest and has been correctly predicted as such (true positive, TP), it belongs to the background and has been correctly predicted as such (true negative, TN), or it has been incorrectly predicted as an object pixel (false positive, FP) or as a background pixel (false negative, FN). Using these four categories of pixels, we computed common measures of performance [1]}, [2]}, depending on the experiment: sensitivity (Se), specificity (Sp), accuracy (Acc), Dice/F1-score (F1). For the skin lesion segmentation experiment, we used the F1 and the Jaccard coefficient (Jacc) so as to allow direct comparison with methods in literature[3]}. In the retinal vessel segmentation experiment, we also made use of the area under the receiver operating characteristic curve (AUC), as the datasets have an unbalanced distribution of positive and negative classes and AUC has been found to be a good measure to gauge how the model can separate those classes in segmentation problems [4]}.
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